Gerd Gigerenzer has worked in many areas, from the history of probability
theory and statistical inference to deductive reasoning in social contexts,
but is perhaps best known for his theoretical and empirical work on judgment
under uncertainty, and for his recent focus on applying evolutionary perspectives
to cognitive science and decision-making. His theoretical analyses
have led to a reevaluation of the traditional cognitive practice of defining
human rationality as a strict adherence to existing normative theories
drawn from mathematics and logic, especially when applied without an analysis
of the structure of the domain to which they are applied. His empirical
work has shown that, contrary to widespread belief in psychology, people
are very good "intuitive probabilists" -- if they are provided with
information in an ecologically valid format.

The work Gigerenzer will be talking about on Wednesday goes well beyond
the prior research that demonstrated that people are good at solving Bayesian
reasoning problems when asked to reason about event frequencies. He has
found evidence that people make judgments using algorithms that are "fast
and frugal". These algorithms differ from the kind of heuristics
suggested by Tversky, Kahneman, and others, in a very important manner:
rather than being "quick and dirty", these algorithms are "quick and clean".
I.e., they produce very well-calibrated judgments - in many cases, judgments
that are as good or better than those generated by more sophisticated computational
methods that attempt to integrate more types of information.

This work has important implications for cognitive scientists, economists,
and also for biologists working on foraging or other problems involving
information-processing.